Due to the problem of drinking water scarcity in different cities around the world, there are innovative proposals to automate garden irrigation in homes, to reduce drinking water consumption. For this research, a sample of 68 inhabitants of the Region of Arequipa - Peru has been surveyed to know the common habits in the irrigation of the gardens. From this data, two systems have been implemented in two average gardens using the Arduino UNO board (integrating with the Ethernet Shield) and the NodeMCU, each proposal integrates soil moisture sensors, water flow sensor, and actuators, such as the solenoid valve and the relay, besides centralizing the information through an IoT System (Home Assistant or Adafruit IO). This has managed to establish a comparison of both, generating a discussion according to the advantages and disadvantages addressed by each proposal and obtaining a saving of potable water in the irrigation of plants.
We introduce and study an artificial neural network inspired by the probabilistic receptor affinity distribution model of olfaction. Our system consists of N sensory neurons whose outputs converge on a single processing linear threshold element. The system's aim is to model discrimination of a single target odorant from a large number p of background odorants within a range of odorant concentrations. We show that this is possible provided p does not exceed a critical value p(c) and calculate the critical capacity alpha(c) = p(c)/N. The critical capacity depends on the range of concentrations in which the discrimination is to be accomplished. If the olfactory bulb may be thought of as a collection of such processing elements, each responsible for the discrimination of a single odorant, our study provides a quantitative analysis of the potential computational properties of the olfactory bulb. The mathematical formulation of the problem we consider is one of determining the capacity for linear separability of continuous curves, embedded in a large-dimensional space. This is accomplished here by a numerical study, using a method that signals whether the discrimination task is realizable, together with a finite-size scaling analysis.
Due to the COVID-19 pandemic, the world's population has undergone different changes in which an impact on the use of the internet stands out from daily aspects such as education, commerce, health, among others; which has made us highly dependent on its usage. In this regard, the term Internet of Things (IoT) has been increasingly recognized for its contribution to improving people's life quality, through objects that are integrated and connected to the Internet. This document aims to introduce alternatives for IoT-based home automation making use of NodeMCU, Sinric Pro, and smart voice assistants such as Google Home and Alexa. The proposals are efficient in terms of easy implementation and reduce electricity consumption by around 30%. This document is research that helps families improve their energy efficiency and daily productivity through IoT.
The current situation in the region of Arequipa (Peru) is an increase in crime and insecurity; companies that provide private surveillance services have increased the costs of equipment and services online. We propose is to implement a low-cost gas leakage and surveillance system for single-family houses, implemented with Raspberry Pi3, an Arduino board, SIM 900 module, sensors, actuators, and peripherals. The system alerts by sending a text message when an intruder enters the home or when there is a gas leak; it captures the webcam image that is sent to the homeowner's email. For voice command recognition, Wit.ai and Firebase are used for communication between the system and the mobile application. System functionality and usability tests were carried out, allowing us to know user satisfaction.
There are several clustering algorithms that yield different grouping results; thus, it is necessary to choose an algorithm that offers the best results for the segmentation of students. Herein, a comparative study of unsupervised data mining techniques is conducted for the segmentation of students according to their academic performance using algorithms such as If-means and PAM within partition clustering and methods such as Ward, single, complete, average, Mcquitty, median and centroid of hierarchical clustering agglomerative. Then, a data mining algorithm is chosen based on the best grouping quality that is obtained using internal measures, such as intra-cluster and intercluster distances, and the silhouette coefficient, thereby obtaining improved results with the partition-clustering technique If-means for the segmentation of students in three groups that can be used to reinforce student learning at the basic, intermediate, and advanced levels.
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